Plotting Scenario Data

The plotting functions use the analysis modules to process data. We have a demo folder that encloses numerous notebooks that we hope will help you analyze/display your scenario data.

Single Scenario

Transmission

• get a power flow snapshot (notebook)

  import pandas as pd
  from bokeh.io import show
  from powersimdata import Scenario
  
  from postreise.plot.plot_powerflow_snapshot import plot_powerflow_snapshot
  
  scenario = Scenario(3287)
  grid = scenario.get_grid()
  
  pf_map = plot_powerflow_snapshot(
      scenario, pd.Timestamp(2016, 11, 2, 22), legend_font_size=20
  )
  show(pf_map)
  

  

• get utilization map (notebook)

  from bokeh.io import show
  from powersimdata import Scenario
  
  from postreise.plot.plot_utilization_map import map_utilization
  
  scenario = Scenario("3287")
  util_map = map_utilization(scenario, state_borders_kwargs={"background_map": False})
  show(util_map)
  

Emission

• show carbon emission by generator type using circles overlay on a map (notebook)

  from bokeh.io import show
  from powersimdata import Scenario
  
  from postreise.plot.plot_carbon_map import map_carbon_emission_generator
  
  scenario = Scenario(3287)
  
  emission_map = map_carbon_emission_generator(
      scenario, coordinate_rounding=0, scale_factor=0.75
  )
  show(emission_map)
  

Generator Capacity and Generation

• plot stacked generation time series in an area (notebook)

  from powersimdata import Scenario
  
  from postreise.plot.plot_generation_ts_stack import plot_generation_time_series_stack
  
  t2c = {
      "nuclear": "#173FA5",
      "hydro": "#0090FF",
      "geothermal": "#CC67F3",
      "other": "#8B36FF",
      "dfo": "#31E8CB",
      "coal": "#37404C",
      "ng": "#72818F",
      "solar": "#FFBB45",
      "wind": "#78D911",
      "solar_curtailment": "#FFBB45",
      "wind_curtailment": "#78D911",
  }
  
  t2l = {
      "nuclear": "Nuclear",
      "hydro": "Hydro",
      "geothermal": "Geothermal",
      "other": "Other",
      "dfo": "Distillate Fuel Oil",
      "coal": "Coal",
      "ng": "Natural Gas",
      "solar": "Solar",
      "wind": "Wind",
      "wind_offshore": "Wind Offshore",
      "biomass": "Biomass",
      "storage": "Storage",
      "solar_curtailment": "Solar Curtailment",
      "wind_curtailment": "Wind Curtailment",
      "wind_offshore_curtailment": "Offshore Wind Curtailment",
  }
  
  t2hc = {"solar_curtailment": "#996100", "wind_curtailment": "#4e8e0b"}
  
  scenario = Scenario(1171)
  
  resources = [
      "nuclear",
      "coal",
      "hydro",
      "geothermal",
      "other",
      "dfo",
      "ng",
      "solar",
      "wind",
      "storage",
      "solar_curtailment",
      "wind_curtailment",
  ]
  
  plot_generation_time_series_stack(
      scenario,
      "Western",
      resources,
      time_freq="D",
      normalize=True,
      t2c=t2c,
      t2l=t2l,
      t2hc=t2hc,
  )
  

  

• plot capacity vs capacity factor of generators in an area (notebook)

  from powersimdata import Scenario
  from powersimdata.utility.helpers import PrintManager
  
  from postreise.plot.plot_scatter_capacity_vs_capacity_factor import (
      plot_scatter_capacity_vs_capacity_factor,
  )
  
  with PrintManager():
      scenario = Scenario(1171)
      plot_scatter_capacity_vs_capacity_factor(
          scenario, "Western", "solar", percentage=True
      )
  

  

Curtailment

• plot renewable generators curtailment time series in an area (notebook)

  import matplotlib.pyplot as plt
  from powersimdata import Scenario
  
  from postreise.plot.plot_curtailment_ts import plot_curtailment_time_series
  
  scenario = Scenario(403)
  
  t2c = {"wind_curtailment": "blue", "solar_curtailment": "blue"}
  
  plot_curtailment_time_series(
      scenario,
      "Eastern",
      ["solar", "wind"],
      time_freq="D",
      t2c=t2c,
      label_fontsize=30,
      title_fontsize=35,
      tick_fontsize=25,
      legend_fontsize=25,
  )
  plt.show()
  

  

• plot capacity vs curtailment of generators in an area (notebook)

  from powersimdata import Scenario
  from powersimdata.utility.helpers import PrintManager
  
  from postreise.plot.plot_scatter_capacity_vs_curtailment import (
      plot_scatter_capacity_vs_curtailment,
  )
  
  with PrintManager():
      scenario = Scenario(3287)
      plot_scatter_capacity_vs_curtailment(scenario, "Western", "solar", percentage=True)
  

  

Price

• map locational marginal price (notebook)

  from bokeh.io import show
  from powersimdata import Scenario
  
  from postreise.plot.plot_lmp_map import map_lmp
  
  scenario = Scenario(3287)
  
  lmp_map = map_lmp(scenario, lmp_min=10, lmp_max=35, num_ticks=6, scale_factor=1.5)
  show(lmp_map)
  

• plot capacity vs cost curve slope of generators in an area (notebook)

  from powersimdata import Scenario
  from powersimdata.utility.helpers import PrintManager
  
  from postreise.plot.plot_scatter_capacity_vs_cost_curve_slope import (
      plot_scatter_capacity_vs_cost_curve_slope,
  )
  
  with PrintManager():
      scenario = Scenario(3287)
      plot_scatter_capacity_vs_cost_curve_slope(scenario, "Eastern", "coal")
  

  

General

• plot any time-series values using a heatmap where each column is one color-coded day (notebook)

  from powersimdata import Scenario
  
  from postreise.analyze.generation.curtailment import calculate_curtailment_time_series
  from postreise.plot.plot_heatmap import plot_heatmap
  
  scenario = Scenario(3287)
  curtailment = calculate_curtailment_time_series(scenario).sum(axis=1)
  
  plot_heatmap(
      curtailment,
      cmap="PiYG_r",
      scale=1e-3,
      cbar_label="GW",
      vmin=0,
      vmax=250,
      cbar_tick_values=[0, 50, 100, 150, 200, 250],
      cbar_tick_labels=["0", "50", "100", "150", "200", "≥250"],
      time_zone="ETC/GMT+6",
      time_zone_label="(CST)",
      contour_levels=[250],
  )
  

  

• map transmission lines color coded by interconnection

  from bokeh.io import show
  from powersimdata import Scenario
  
  from postreise.plot.plot_interconnection_map import map_interconnections
  
  scenario = Scenario(3287)
  grid = scenario.get_grid()
  
  transmission_map = map_interconnections(grid)
  show(transmission_map)
  

Scenarios Comparison

Generator Capacity and Generation

• compare generation and capacity in various scenarios through bar charts (notebook)

  from powersimdata.utility.helpers import PrintManager
  
  from postreise.plot.plot_bar_generation_vs_capacity import (
      plot_bar_generation_vs_capacity,
  )
  
  with PrintManager():
      plot_bar_generation_vs_capacity(
          areas=["CA", "Western"],
          scenario_ids=[2497, 3101],
          scenario_names=[
              "Western 90% clean and 10% nuclear",
              "Western 90% clean and 10% nuclear with transmission upgrade",
          ],
      )
  

  

• compare generation and capacity in various scenarios through pie charts (notebook)

  from powersimdata.utility.helpers import PrintManager
  
  from postreise.plot.plot_pie_generation_vs_capacity import (
      plot_pie_generation_vs_capacity,
  )
  
  with PrintManager():
      plot_pie_generation_vs_capacity(
          areas=["WA", "Western"],
          scenario_ids=[2497, 3101],
          scenario_names=[
              "Western 90% clean and 10% nuclear",
              "Western 90% clean and 10% nuclear \n with transmission upgrade",
          ],
      )
  

  

• compare generation shortfall in various scenarios through bar charts (notebook)

  import inspect
  import os
  
  from powersimdata.design.generation.clean_capacity_scaling import load_targets_from_csv
  from powersimdata.utility.helpers import PrintManager
  
  import postreise
  from postreise.plot.plot_bar_shortfall import plot_bar_shortfall
  
  data = os.path.join(os.path.dirname(inspect.getfile(postreise)), "data")
  target = load_targets_from_csv(
      os.path.join(data, "2030_USA_Clean_Energy_Regular_Targets.csv")
  )
  with PrintManager():
      plot_bar_shortfall(
          "Nevada",
          [2497, 3101],
          target,
          scenario_names=[
              "Western 90% clean and 10% nuclear",
              "Western 90% clean and 10% nuclear" + "\n" + "with transmission upgrade",
          ],
          baseline_scenario=2497,
          baseline_scenario_name="Western 90% clean and 10% nuclear",
      )
  

  

Emission

• compare total carbon emissions by generator type for 1-to-n scenarios through bar charts (notebook)

  from powersimdata import Scenario
  from powersimdata.utility.helpers import PrintManager
  
  from postreise.plot.plot_carbon_bar import plot_carbon_bar
  
  scenarioA = Scenario(2497)
  scenarioB = Scenario(3101)
  
  with PrintManager():
      scenarioA = Scenario(2497)
      scenarioB = Scenario(3101)
  
  plot_carbon_bar(
      scenarioA,
      scenarioB,
      labels=[
          "Western" + "\n" + "90% clean and 10% nuclear",
          "Western"
          + "\n"
          + "90% clean and 10% nuclear"
          + "\n"
          + "with transmission upgrade",
      ],
  )
  

  

• compare carbon emission by generator type for two scenarios on a map (notebook)

  from bokeh.io import show
  from powersimdata import Scenario
  
  from postreise.plot.plot_carbon_map import map_carbon_emission_difference
  
  scenarioA = Scenario(2497)
  scenarioB = Scenario(3101)
  
  emission_difference_map = map_carbon_emission_difference(
      scenarioA, scenarioB, coordinate_rounding=1
  )
  show(emission_difference_map)
  

• plot stacked generation and carbon emission for 1-to-n scenarios side-by-side (notebook)

  from powersimdata import Scenario
  
  from postreise.plot.plot_energy_carbon_stack import plot_n_scenarios
  
  scenarioA = Scenario(2497)
  scenarioB = Scenario(3101)
  
  plot_n_scenarios(scenarioA, scenarioB)